There are many applications for computer systems that are able to generate recognisable images of individuals. These applications range from systems for displaying the face of a caller on a telephone through to computer graphics generated within computer games. Although model-based methods for representing faces exist, existing methods typically require a relatively large number of parameters in order to deal with the variation that exists in human faces.
One known method of modelling human faces is using principle component analysis. In order to generate a model of the way in which faces vary, a large data set of different faces is first obtained. Feature points on the faces are then identified so that an average face can be determined. The manner in which each individual face used to generate the model varies from this average face can then be identified and the results subjected to principle component analysis to determine the most significant ways in which faces within the data set vary.
By generating a model of an individual face using a limited number of the most significant variations, a reasonable approximation of a specific individual face can be generated.
Although a computer model derived from principle component analysis of a large number of faces can be used to form a relatively compact representation of a particular person, it is desirable to provide a system in which a high quality model of an individual face can be represented in as few parameters as possible. Further it is desirable that a model of an individual face can be generated quickly and easily.